One of R's strengths is data visualization.

R can create static as well as interactive graphs with a rich set of user-contributed packages

• Static graph systems
  • Base R graphics
  • lattice (Sarkar, et al)
  • ggplot2 (Wickham)
• Dynamic graphs
  • rCharts
  • leaflet
  • plotly
  • Many others

We're making the decision to use ggplot2 for my graphics

• Makes pretty good formatting choices out of the box
• Is declarative (tell it what you want) without getting caught up in minutae
• Strongly leverages data frames (good practice)
• Fast enough
• There are good templates if you want to change the look

If you haven't installed it yet:

install.packages('ggplot2', 
                 repos="http://watson.nci.nih.gov/cran_mirror/")

then

library(ggplot2)

The ggplot2 package is a very flexible and (to me) intuitive way of visualizing data. It is based on the concept of layering elements on a canvas.

You need:

• A data.frame object
• Aesthetic mappings (aes) to say what data is used for what purpose in the viz
  • x- and y-direction
  • shapes, colors, lines
• A geometry object (geom) to say what to draw
  • You can "layer" geoms on each other to build plots

library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()

• A data.frame object: mtcars
• Aesthetic mapping: x-axis: wt y-axis: mpg
• Geometry:
  • geom_point: draw points

library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()+ geom_smooth()

• A data.frame object: mtcars
• Aesthetic mapping: x-axis: wt y-axis: mpg
• Geometry:
  • geom_point: draw points
  • geom_smooth: Add a layer which draws a best-fitting line

We will use the two data sets:

data_spine <- read.csv('http://www.araastat.com/BIOF339_PracticalR/
                       Lectures/lecture2_data/Dataset_spine.csv', 
                       stringsAsFactors = F)

data_brca <- read.csv('http://www.araastat.com/BIOF339_PracticalR/
                      Lectures/lecture2_data/
                      clinical_data_breast_cancer_modified.csv',
                      stringsAsFactors = F)

ggplot(data_brca, aes(x = Age.at.Initial.Pathologic.Diagnosis)) + 
  geom_histogram()

#   `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data_brca, aes(x = Age.at.Initial.Pathologic.Diagnosis)) + 
  geom_histogram(binwidth=4)

ggplot(data_brca, aes(x = Age.at.Initial.Pathologic.Diagnosis)) + 
  geom_density()

ggplot(data_brca, aes(x = Tumor))+geom_bar()

Using the mtcars dataset in R, create:

1. A histogram of the fuel efficiences (mpg) in the data set
2. A bar plot of frequencies of number of cylinders (cyl) in the car

ggplot(mtcars, aes(x = mpg)) + geom_histogram(binwidth=3)

# ggplot(mtcars) + geom_histogram(aes(x = mpg), binwidth = 3)

ggplot(mtcars, aes(x = factor(cyl))) + geom_bar()

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope)) + 
  geom_point()

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope))+
  geom_point() + 
  geom_smooth()

#   `geom_smooth()` using method = 'loess'

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope)) +
  geom_point()+
  geom_smooth(method='lm')

library(forecast)
d <- data.frame(x = 1:length(gas), y = gas) # Australian monthly gas production
ggplot(d, aes(x, y)) + geom_line()

1. Create a scatter plot of sepal length and sepal width from the iris dataset, and add a smooth line through it

ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() + geom_smooth()

#   `geom_smooth()` using method = 'loess'

ggplot(data_spine, aes(x = Class.attribute, y = Sacral.slope))+
  geom_boxplot()

# Factor/discrete variable is always x

ggplot(data_spine, aes(x = Class.attribute, y = Sacral.slope)) +
  geom_violin()

1. Plot a boxplot of petal length by species using the iris dataset

ggplot(iris, aes(x = Species, y = Petal.Length))+geom_boxplot()

ggplot(data_brca, aes(x = Tumor))+geom_bar()

ggplot(data_brca, aes(x = Tumor))+geom_bar()+
  coord_flip()

• The ggplot website has many resources to help create visualizations
• There are a lot of blogs showing many capabilities of ggplot2
• StackOverflow is the place for Q & A.

• It is common to look at statistics within subgroups of the data
• The idea is to see if secondary variables affect your primary outcome or relationship

dplyr is the most lucid package for manipulating and analyzing data organized in a data frame.

• It has a group_by function which creates a grouped data frame

library(dplyr)
grouped_data_spine = group_by( data_spine, Class.attribute)

Note that you have to group using a discrete valued variable (factor, character, integer)

summarize(grouped_data_spine, 
          mean(Pelvic.incidence), 
          sd(Pelvic.incidence),
          min(Pelvic.incidence),
          max(Pelvic.incidence))

summarize(grouped_data_spine, 
          Mean = mean(Pelvic.incidence), 
          SD = sd(Pelvic.incidence),
          Min = min(Pelvic.incidence),
          Max = max(Pelvic.incidence))

summarize_all(grouped_data_spine, mean)

#   # A tibble: 2 x 13
#     Class.attribute Pelvic.incidence Pelvic.tilt Lumbar.lordosis.angle
#              <fctr>            <dbl>       <dbl>                 <dbl>
#   1        Abnormal         64.69256    19.79111              55.92537
#   2          Normal         51.68524    12.82141              43.54260
#   # ... with 9 more variables: Sacral.slope <dbl>, Pelvic.radius <dbl>,
#   #   Degree.spondylolisthesis <dbl>, Pelvic.slope <dbl>, Direct.tilt <dbl>,
#   #   Thoracic.slope <dbl>, Cervical.tilt <dbl>, Sacrum.angle <dbl>,
#   #   Scoliosis.slope <dbl>

• Tibbles are a new-generation object meant to enhance the data.frame.
• Central to the so-called tidyverse packages
• If you want to just get back to a more familiar data.frame object, use as.data.frame
• A tibble is built on a data.frame, so all operations on data.frame's will work.
• To see all columns, set options(dplyr.width=Inf).

Differences between a tibble and a data.frame:

1. Printing a tibble is restricted to the first 10 lines, and includes column types
2. Stricter subsetting rules that make the types of objects created consistent

ggplot(data_spine, aes(x = Sacral.slope, group = Class.attribute, color=Class.attribute))+
  geom_density()

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope,
                       group = Class.attribute, color = Class.attribute))+
  geom_point()

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope, 
                       group = Class.attribute, shape = Class.attribute))+
  geom_point()

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope))+
  geom_point(aes(size = Pelvic.slope))

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope, 
                       group = Class.attribute, color = Class.attribute))+
  geom_point(aes(size = Pelvic.slope))

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope, 
                       group = Class.attribute, color=Class.attribute))+
  geom_point()+
  geom_smooth(method='lm')

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope))+
  geom_point()+
  geom_smooth(aes(color = Class.attribute), method='lm')

Facetted graphs are a panel of graphs, each of which corresponds to a particular subgroup of the data.

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope))+
  geom_point()+
  facet_wrap( ~ Class.attribute, nrow=1)

ggplot(data_spine, aes(x = Lumbar.lordosis.angle, y = Sacral.slope))+
  geom_point()+ geom_smooth(method='lm')+
  facet_wrap( ~ Class.attribute, nrow=1)

library(qqman)
data(gwasResults)
head(gwasResults)

#     SNP CHR BP         P
#   1 rs1   1  1 0.9148060
#   2 rs2   1  2 0.9370754
#   3 rs3   1  3 0.2861395
#   4 rs4   1  4 0.8304476
#   5 rs5   1  5 0.6417455
#   6 rs6   1  6 0.5190959

gwasResults = transform(gwasResults, x_position = 1:nrow(gwasResults))

ggplot(gwasResults, aes(x = x_position, y = -log(P, base=10)))+
  geom_point(size=0.2)

ggplot(gwasResults, aes(x = x_position, y = -log(P, base=10),
                        group=CHR, color=CHR))+
  geom_point(size=0.2)

ggplot(gwasResults, aes(x = x_position, y = -log(P, base=10),
                        group=factor(CHR), color=factor(CHR)))+
  geom_point(size=0.2)

ggplot(gwasResults, aes(x = x_position, y = -log(P, base=10),
                        group=factor(CHR), color=factor(CHR)))+
  geom_point(size=0.2)+
  geom_hline(yintercept = 8, color='red', linetype=2)

ggplot(gwasResults, aes(x = BP, y = -log(P, base=10)))+
  geom_point(size=0.2)+
  facet_wrap(~ CHR, nrow=4)+
  geom_hline(yintercept = 8, color='red', linetype=2)